(Antibiotic-Resistant) E. coli in the Dutch–German Vecht Catchment—Monitoring and Modeling

Fecally contaminated waters can be a source for human infections. We investigated the occurrence of fecal indicator bacteria (E. coli) and antibiotic-resistant E. coli, namely, extended spectrum beta-lactamase (ESBL)-producing E. coli (ESBL-EC) and carbapenemase-producing E. coli (CP-EC) in the Dutch–German transboundary catchment of the Vecht River. Over the course of one year, bacterial concentrations were monitored in wastewater treatment plant (WWTP) influents and effluents and in surface waters with and without WWTP influence. Subsequently, the GREAT-ER model was adopted for the prediction of (antibiotic-resistant) E. coli concentrations. The model was parametrized and evaluated for two distinct scenarios (average flow scenario, dry summer scenario). Statistical analysis of WWTP monitoring data revealed a significantly higher (factor 2) proportion of ESBL-EC among E. coli in German compared to Dutch WWTPs. CP-EC were present in 43% of influent samples. The modeling approach yielded spatially accurate descriptions of microbial concentrations for the average flow scenario. Predicted E. coli concentrations exceed the threshold value of the Bathing Water Directive for a good bathing water quality at less than 10% of potential swimming sites in both scenarios. During a single swimming event up to 61 CFU of ESBL-EC and less than 1 CFU of CP-EC could be taken up by ingestion.

where X 1 is 1 for summer and 0 for the remaining year. This results in log 10 pcL in = 10.558 and leads to pcL in = 3.61 x 10 10 CFU cap -1 d -1 . logRed is calculated with model 4 (Table S11): where X 1 is 1 for summer and 0 for the remaining year and X 2 is the normalized WWTP discharge, i.e. discharge normalized by dry weather flow (DWF). This indicates that treatment efficiency is highest, S14 when WWTP discharge is low and when it is summer. For the dry summer scenario we assume that the discharge is equal to the DWF for all WWTPs in summer. This leads to The fully parametrized WWTP emission model for E. coli in the dry summer scenario for WWTP W01 is then: = 3.61 ⋅ 10 10 ⋅ 32 050 ⋅ 10 -3.048 = 1.04 ⋅ 10 12 In the dry summer scenario, WWTP W01 is predicted to release 1.04 x 10 12 CFU of E. coli per day into the receiving river.    (Table S14).
Due to the large number of non-detects (see Figure S2), parametrization of the ESBL-EC increment concentrations was based on E. coli using relative abundance of ESBL-EC. We assume that ESBL-EC to E.
coli ratios are always the same in all river flow increments. From measured data at the background monitoring sites a median value of 0.14% was derived for this ratio. For CP-EC no such data was available.
Therefore, diffuse emissions of CP-EC were not considered.
For this study, diffuse emissions of bacteria are thought to encompass (i) passive transport by the flow components runoff, interflow, baseflow and (ii) remobilization of bacteria from the sediments. These processes are thought to contribute differently to diffuse emissions and background concentrations in the S18 two modeled scenarios. The exact quantification of the contribution of individual processes however, cannot be provided here due to insufficient data and process understanding.
In the dry summer scenario, where mainly groundwater exchange is responsible for river flow, diffuse emissions of bacteria are thought to mainly account for remobilization of bacteria from the sediments.
Sediments are a reservoir for E. coli bacteria 11  In the average flow scenario, the remobilization of bacteria from the sediments is thought to additionally appear due to bed shear stress due to high flows 6 Figure S7 and spatially resolved as maps in Figures S8 -S9. Excluding emission processes leads lower concentrations and excluding loss processes to higher concentrations ( Figure S7).
For this analysis, we define that if the exclusion of a process leads to a deviation of less than 0.25 log units in concentration compared to the baseline scenario, the river segment is not sensitive towards the excluded process. Consequently, we call a process "sensitive" towards a river segment, if the deviation is larger than 0.25 log units. Additionally, we call a process "very sensitive" towards a river segment, when it increases or decreases simulated concentrations by more than one order of magnitude.  Figures S8 and S9). This is where river segments are most sensitive towards WWTP emissions.
In the model, sedimentation takes place in all river segments. The process depends on the residence time of a segment, calculated by the length of the river segment and the flow velocity as well as on its depth. Both, flow velocity and depth, are generally higher for natural waterbodies in the average flow scenario. Consequently, in the average flow scenario, 72% and 10% of cumulated flow length are sensitive and very sensitive towards sedimentation, whereas in the dry summer scenario 96% and 26% of cumulated flow length are sensitive and very sensitive towards sedimentation (see Figure S7). In canals, the flow velocity is lower compared to natural flowing waterbodies 16 . Therefore, these waterbodies have a comparably longer residence time and are more sensitive towards sedimentation. In the average flow scenario, 96% and 41% of cumulated canal flow length are sensitive and very sensitive towards sedimentation. In the dry summer scenario, flow velocity in some canals can be increased compared to the average flow scenario due to pumping activities. This results in 95% and 32% of cumulated canal flow length being sensitive and very sensitive towards sedimentation.
Just like sedimentation, inactivation is also modeled to occur catchment-wide. In contrast to sedimentation, inactivation is modeled to be independent of the depth of the respective segment.

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Generally the inactivation affects concentrations less than sedimentation in both scenarios ( Figures S8   and S9). 17% and 19% of cumulated flow length are sensitive towards inactivation, for the average flow scenario and the dry summer scenario, respectively. Less than 1% of cumulated flow length is very sensitive towards inactivation in both scenarios.